Statistics for People Who (Think They) Hate Statistics

by Neil J. Salkind

Effect size is the strength of a relationship between variables. It can be a correlation coefficient, as we talked about in Chapter 5, but the relationship between variables can also be apparent in the size of a difference between groups. It could be an indication of how effective a pill or intervention is, right?— a measure of the magnitude of the treatment. So the difference between a group that got a treatment and the group that did not shows the relationship between the independent variable (the treatment) and the dependent variable. So effect sizes can be correlational values or values that estimate difference. What’s especially interesting about computing effect size is that sample size is not taken into account. Calculating effect size, and making a judgment about it, adds a whole new dimension to understanding outcomes that does not require significance. Another interesting note about effect size is that many different inferential tests use different formulas to compute the effect size (as you saw in Chapter 5 and will see through the next few chapters), but one common metric (called Cohen’s d, and we’ll get to that shortly) tends to be used when group differences are examined.